Many industries, including the oil and gas industry, have access to large amounts of electronic data and information, and advanced software tools for displaying various types of information. As the amount of available data increases, the need for software tools to extract, or filter out, the relevant information in a given situation increases correspondingly.
As part of their normal work, oil well drilling engineers and other operational personnel both offshore and in support centres onshore have at their disposal a large set of sophisticated sensor measurements and other drilling parameters. The bulk of this data are continuous (real time) data streams from the drilling operation. Software tools for keeping track of data from these drilling logs help the personnel to perform graphical comparisons through time-indexed or depth-indexed graphs. However, as powerful as these visualisation tools are, the drilling operation is fundamentally reliant on the experience and training of the individual drilling engineer to interpret the data and take appropriate action.
As the world's reserves of fossil fuel diminish, wells are becoming increasingly difficult and correspondingly expensive to drill, and operational mistakes have potentially more serious, not to mention extremely expensive, effects. The typical running costs for an offshore drilling platform can be up to $200,000 per day. Any loss of drilling time caused by unwanted events is undesirable.
Case-based reasoning (CBR) is an approach to problem solving and decision making where a new problem is solved by finding one or more similar previously solved problems, called cases, and re-using them in the new problem situation. It has been recognised that CBR might find a practical application in the offshore drilling industry where there is a wealth of stored information on operational drilling experience from around the world but which drilling engineers find difficult to access and use for the purpose of decision making in real time. In particular, European publication EP1297244 describes a computer implemented CBR system in which a drilling engineer manually enters data describing a current drilling situation into a database query which is used to search for and identify similar past cases stored in a database adapted for CBR. The past cases contain associated drilling data and user experience for a similar drilling situation, typically from a different drilling site, that might help the drilling engineer predict and avoid an unwanted event. The core of this system is the structuring of a knowledge database in order to represent cases as well as general relationships, so that the system can permit the user to manually enter a query in the specified database query language, and get the collection of cases that match the query items in return. The input query is entered by the user, and the retrieved cases are returned, in a structured text format. The database query language allows the user to retrieve cases that perfectly match the query given as input.